import kagglehub
from kagglehub import KaggleDatasetAdapter
import kagglehub

# Download latest version
path = kagglehub.dataset_download("matthieugimbert/french-bakery-daily-sales")

print("Path to dataset files:", path)

/Users/rajivsambasivan/Programming/r2ds-blog-worskpace/r2ds-blog/.venv/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm

Path to dataset files: /Users/rajivsambasivan/.cache/kagglehub/datasets/matthieugimbert/french-bakery-daily-sales/versions/1

import pandas as pd
# kagglehub, please make this simpler, provide some example, really poor documentation for the api"
fp = path + "/Bakery Sales.csv"
df = pd.read_csv(fp)
cols = df.columns.tolist()
cols.remove("Unnamed: 0")
df = df[cols]
df.head()

	date	time	ticket_number	article	Quantity	unit_price
0	2021-01-02	08:38	150040.0	BAGUETTE	1.0	0,90 €
1	2021-01-02	08:38	150040.0	PAIN AU CHOCOLAT	3.0	1,20 €
2	2021-01-02	09:14	150041.0	PAIN AU CHOCOLAT	2.0	1,20 €
3	2021-01-02	09:14	150041.0	PAIN	1.0	1,15 €
4	2021-01-02	09:25	150042.0	TRADITIONAL BAGUETTE	5.0	1,20 €

filter_baguette = (df.article == "BAGUETTE")
df["datetime"] = pd.to_datetime(df["date"]  + " " + df["time"])
df_baguette = df[filter_baguette]
cols = ["datetime", "Quantity"]
df_baguette = df_baguette[cols]

df_daily_baugette = df_baguette.set_index("datetime").resample("D").sum()
df_weekly_baugette = df_baguette.set_index("datetime").resample("W").sum()

df_weekly_baugette = df_weekly_baugette.reset_index()

df_weekly_baugette

	datetime	Quantity
0	2021-01-03	81.0
1	2021-01-10	165.0
2	2021-01-17	168.0
3	2021-01-24	160.0
4	2021-01-31	167.0
...	...	...
87	2022-09-04	205.0
88	2022-09-11	175.0
89	2022-09-18	207.0
90	2022-09-25	141.0
91	2022-10-02	140.0

92 rows × 2 columns

df_weekly_baugette["month"] = df_weekly_baugette.datetime.dt.month
df_weekly_baugette["WOY"] = df_weekly_baugette.datetime.dt.isocalendar().week

df_weekly_baugette["CWOY"] = df_weekly_baugette.apply(lambda x: x["WOY"] + 53 if x.datetime.year == 2022 else x["WOY"], axis=1)

zero_counts = (df_daily_baugette.Quantity == 0)
df_daily_baugette[zero_counts].shape

fp = "../data/weekly_baguette_sales.csv"
df_weekly_baugette.to_csv(fp, index=False)

df_daily_baugette = df_daily_baugette.reset_index()

df_daily_baugette["Day"] = df_daily_baugette.index
df_daily_baugette["DOW"] = df_daily_baugette.datetime.dt.day_of_week
df_daily_baugette["month"] = df_daily_baugette.datetime.dt.month

df_daily_baugette["WOY"] = df_daily_baugette.datetime.dt.isocalendar().week

cols = ["Day","datetime", "DOW", "month", "WOY", "Quantity"]
df_daily_baugette = df_daily_baugette[cols]

df_daily_baugette.shape

(637, 6)

from matplotlib import pyplot as plt

plt.figure(figsize=(12, 6))
df_daily_baugette["Quantity"].plot()

from statsmodels.tsa.seasonal import STL
daily_baugette_sales = pd.Series(df_daily_baugette["Quantity"].values, index=df_daily_baugette["datetime"])
stl = STL(daily_baugette_sales, period=7)
res = stl.fit()

decomp_res = {"Trend": res._trend, "Seasonality": res._seasonal, "Noise": res._resid}
df_res = pd.DataFrame.from_dict(decomp_res, orient="columns")
df_res = df_res.reset_index()

df_res

	datetime	Trend	Seasonality	Noise
0	2021-01-02	28.179642	11.289480	6.530878
1	2021-01-03	27.405646	10.434721	-2.840368
2	2021-01-04	26.674892	6.537131	-3.212023
3	2021-01-05	25.977024	2.894178	0.128798
4	2021-01-06	25.312682	-24.668598	-0.644084
...	...	...	...	...
632	2022-09-26	27.396448	-15.522747	6.126299
633	2022-09-27	28.848273	2.410161	2.741566
634	2022-09-28	30.336600	-8.081090	0.744491
635	2022-09-29	31.840976	6.798708	-8.639685
636	2022-09-30	33.351069	-3.845030	5.493961

637 rows × 4 columns

df_daily_baugette["Trend"] = df_res["Trend"].round(3)
df_daily_baugette["Seasonality"] = df_res["Seasonality"].round(3)

zero_counts = (df_daily_baugette.Quantity == 0)
df_daily_baugette[zero_counts].shape

(37, 8)

# Using plotly.express
import plotly.express as px
fig = px.line(df_daily_baugette, x='datetime', y="Quantity", markers=True)
fig.update_layout(
    autosize=False,
    width=1100,
    height=800,
)
fig.show()

# Using plotly.express
import plotly.express as px
fig = px.line(df_res, x='datetime', y="Trend", title="Trend Cycle Component of Daily Baugette Sales",
             labels = {"Trend": "Number of Baugettes", "datetime": "date"})# Using plotly.express
fig.update_layout(
    autosize=False,
    width=1100,
    height=800,
)
fig.show()

# Using plotly.express
import plotly.express as px
fig = px.line(df_res, x='datetime', y="Seasonality", title="Seasonality Component of Daily Baugette Sales",
             labels = {"Trend": "Number of Baugettes", "datetime": "date"})# Using plotly.express
fig.update_layout(
    autosize=False,
    width=1100,
    height=800,
)
fig.show()

# Using plotly.express
import plotly.express as px
fig = px.line(df_res, x='datetime', y="Noise", title="Noise Component of Daily Baugette Sales",
             labels = {"Noise": "Number of Baugettes", "datetime": "date"})# Using plotly.express
fig.update_layout(
    autosize=False,
    width=1100,
    height=800,
)
fig.show()

df_res["Trend"].plot.kde()
plt.grid(True)

df_res["Seasonality"].plot.kde()
plt.grid(True)

import statsmodels.api as sm

plt.rc("figure", figsize=(12,8))
acf_plot = sm.graphics.tsa.plot_acf(df_daily_baugette["Quantity"] , lags=40)
plt.grid(True)

plt.rc("figure", figsize=(12,8))
pacf_plot = sm.graphics.tsa.plot_pacf(df_daily_baugette["Quantity"], lags=40, method="ywm")
plt.grid(True)

df_baguette = df_baguette.reset_index(drop=True)

df_baguette = df_baguette.rename(columns={"index": "Day"})

df_baguette["Day"] = df_baguette.index + 1
df_baguette["DOW"] = df_baguette.datetime.dt.day_of_week
df_baguette["month"] = df_baguette.datetime.dt.month

df_daily_baugette["CWOY"] = df_daily_baugette.apply(lambda x: x["WOY"] + 53 if x.datetime.year == 2022 else x["WOY"], axis=1)

df_daily_baugette["CWOY"].max()

df_daily_baugette

df_period_perf  = pd.pivot_table(df_daily_baugette, index= "CWOY", columns= "DOW", values = "Quantity", fill_value=0) 

df_period_perf = df_period_perf.apply(lambda x: x.div(x.sum()), axis=1).round(3).reset_index()

df_period_perf = df_period_perf.melt(id_vars=["CWOY"], value_vars=[i for i in range(7)], value_name="prop")

df_period_perf

df_daily_baugette = df_daily_baugette.merge(df_period_perf, on = ["CWOY", "DOW"])

df_daily_baugette.columns

cols_for_modeling = df_daily_baugette.columns.tolist()
cols_for_modeling = [c for c in cols_for_modeling if c not in ["datetime", "WOY", "Trend", "Seasonality"]]
df_daily_baugette = df_daily_baugette[cols_for_modeling]
fp = "../data/daily_baugette_sales.csv"
df_daily_baugette.to_csv(fp, index=False)

# Using plotly.express
import plotly.express as px
fig = px.scatter(df_weekly_baugette, x='CWOY', y="Quantity")
fig.update_layout(
    autosize=False,
    width=1100,
    height=800,
)
fig.show()